Abstract
Over the past decade, there have been an increasing number of studies that have investigated problematic and/or ‘addictive’ smartphone use. The present study explored the prevalence and correlates of problematic smartphone use (PSU) among Chinese university students. Few studies have investigated relationships between PSU and factors such as academic anxiety, academic procrastination, self-regulation, and subjective wellbeing. The present study proposed and tested a hypothetical model of relationships between PSU and these factors. A total of 475 Chinese university students completed a paper-based survey during class breaks. The survey comprised a battery of psychometric scales translated into Chinese translations examining the study variables (i.e., academic anxiety, academic procrastination, self-regulation, life satisfaction, and PSU). Path analysis was applied to test the hypothetical model. A good model fit was found (CFI = 1.00, RMSEA = 0.008), in which PSU predicted academic procrastination (β = 0.21, p < 0.001) and academic anxiety (β = 0.18, p < 0.01). Also, self-regulation predicted PSU (β = − 0.35, p < 0.001), academic anxiety (β = − 0.29, p < 0.001), academic procrastination (β = 0.23, p < 0.001) and life satisfaction (β = 0.23, p < 0.001). PSU mediated the relationships between self-regulation, and both academic anxiety and academic procrastination. The present study enhances our understanding of the role of problematic smartphone use in relation to academic behaviour, mental health and wellbeing of college students.
Highlights
Because of the high rate of smartphone possession among Chinese undergraduates (99.2% reported by Long et al (2016)), the present study focuses on smartphone use (i.e. Wi-Fi enabled mobile phones) rather than mobile phone use and will use the term ‘problematic smartphone use’ (PSU)
The present study found that PSU predicted academic procrastination, and that the association was partially mediated by academic anxiety
Smartphone applications (i.e. ‘apps’) designed for time management (e.g. QualityTime) can be used in future studies. Participants might change their behaviours on smartphones when they know their behaviour is being recorded via an app
Summary
ParticipantsA total of 475 undergraduate students were recruited via convenience sampling at a university in South China. The average age of participants was 19.77 years (SD = 1.11), ranging from 16. The participants were studying a range of subjects including English, administrative management, software engineering, media and mechanics. Smartphone Addiction Scale – Short Version (SAS-SV; Kwon et al 2013) PSU was assessed using the SAS-SV. ‘My smartphone is on my mind even when I am not using it’) and is answered using a 6-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (6). Given that the current study was carried out among Chinese students, items related to Twitter and Facebook in the original version were changed into ‘WeChat or other social media’. The cut-off points for smartphone addiction were those used by the scale developers (i.e. 33 out of 50 for females and 31 out of 50 for males).
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